首页> 外文会议>Conference on Intelligent Text Processing and Computational Linguistics;CICLing 2014 >A Machine Learning Approach to Pronominal Anaphora Resolution in Dialogue Based Intelligent Tutoring Systems
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A Machine Learning Approach to Pronominal Anaphora Resolution in Dialogue Based Intelligent Tutoring Systems

机译:基于对话的智能辅导系统中的多种神经分辨率的机器学习方法

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Anaphora resolution is a central topic in dialogue and discourse that deals with finding the referent of a pronoun. It plays a critical role in conversational Intelligent Tutoring Systems (ITSs) as it can increase the accuracy of assessing students’ knowledge level, i.e. mental model, based on their natural language inputs. Although anaphora resolution is one of the most studied problems in Natural Language Processing, there are very few studies that focus on anaphora resolution in dialogue based ITSs. To this end, we present Deep Anaphora Resolution Engine++ (DARE++) that adapts and extends existing machine learning solutions to resolve pronouns in ITS dialogues. Experiments showed that DARE++ achieves a F-measure of 88.93%, proving the potential of the proposed method for resolving pronouns in student-tutor dialogues.
机译:Anaphora解决方案是对话和话语中的一个核心话题,涉及发现代词的指代。 它在会话智能辅导系统(ITS)中起着关键作用,因为它可以提高评估学生知识水平的准确性,即心智模型,基于他们的自然语言输入。 虽然Anaphora解决方案是自然语言处理中最受研究的问题之一,但很少有几次研究,重点关注基于对话中的视为神视图。 为此,我们呈现了深度宣传器分辨率引擎++(敢于++),这些引擎++适应并扩展现有的机器学习解决方案以在其对话中解析代词。 实验表明,Dare ++实现了88.93%的F-Degure,证明了在学生导师对话中解决代词的提议方法的潜力。

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